mlsanityData to 14 Oct 2026

Discovery of False Data Injection Schemes on Frequency Controllers with Reinforcement Learning

arXiv 2408.16958 ↗First posted 30 Aug 2024cs.LG cs.AIcited by 2

On the map: Deep reinforcement learning methods → RL for energy, networks and operations

Abstract (arXiv)

While inverter-based distributed energy resources (DERs) play a crucial role in integrating renewable energy into the power system, they concurrently diminish the grid's system inertia, elevating the risk of frequency instabilities. Furthermore, smart inverters, interfaced via communication networks, pose a potential vulnerability to cyber threats if not diligently managed. To proactively fortify the power grid against sophisticated cyber attacks, we propose to employ reinforcement learning (RL) to identify potential threats and system vulnerabilities. This study concentrates on analyzing adversarial strategies for false data injection, specifically targeting smart inverters involved in primary frequency control. Our findings demonstrate that an RL agent can adeptly discern optimal false data injection methods to manipulate inverter settings, potentially causing catastrophic consequences.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods. Builds on 4 of its 19 references that are in the map. First posted on arXiv 30 Aug 2024.

Builds on

4 of 19 references.

References · 4

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2016Asynchronous Methods for Deep Reinforcement Learningcited by 2,258
  3. 2021The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Gamescited by 942
  4. 2020Reinforcement Learning for Optimal Primary Frequency Control: A Lyapunov Approachcited by 14

Built on by

Cited by year

Citing papers per year: 2024: 2.

2 papers.

  1. 2024Detection of False Data Injection Attacks (FDIA) on Power Dynamical Systems With a State Prediction Methodcited by 2
  2. 2024Continual Adversarial Reinforcement Learning (CARL) of False Data Injection detection: forgetting and explainabilitycited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024Adversarial Multi-Agent Reinforcement Learning for Proactive False Data Injection Detection ↗
  2. 2024DRL2FC: An Attack-Resilient Controller for Automatic Generation Control Based on Deep Reinforcement Learning
  3. 2024A Novel Bifurcation Method for Observation Perturbation Attacks on Reinforcement Learning Agents: Load Altering Attacks on a Cyber Physical Power System
  4. 2024Learning-Enabled Adaptive Voltage Protection Against Load Alteration Attacks On Smart Grids ↗
  5. 2024Reinforcement Learning-Based Approaches for Enhancing Security and Resilience in Smart Control: A Survey on Attack and Defense Methods
  6. 2026Utilizing Adversarial Training for Robust Voltage Control: An Adaptive Deep Reinforcement Learning Method
  7. 2024Safe Reinforcement Learning for Power System Control: A Review
  8. 2024Deep Reinforcement Learning for Optimizing Inverter Control: Fixed and Adaptive Gain Tuning Strategies for Power System Stability